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Robust bivariate random-effects model for accommodating outlying and influential studies in meta-analysis of
Zelalem F Negeri1, Joseph Beyene1,2
1Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada.
Statistical Methods in Medical Research
|May 30, 2020
Summary
A new robust bivariate random-effects model provides reliable meta-analysis of diagnostic test accuracy, even with influential studies. This method ensures accurate sensitivity (Se) and specificity (Sp) estimates by down-weighting outliers.
Area of Science:
- Biostatistics
- Medical Informatics
- Epidemiology
Background:
- Meta-analysis of diagnostic test accuracy studies commonly uses bivariate random-effects models.
- These models assume a bivariate normal distribution for random effects.
- Outlying studies can disproportionately influence parameter estimates, leading to misleading conclusions.
Purpose of the Study:
- To develop a robust bivariate random-effects model that accommodates outlying and influential observations in diagnostic test accuracy meta-analyses.
- To provide robust statistical inference by down-weighting the impact of such studies.
Main Methods:
- Derived the marginal model and Monte Carlo expectation-maximization algorithm for the proposed robust model.
- Conducted a simulation study to validate the new method against standard approaches.
- Applied the model to two published meta-analyses for illustration.
Main Results:
- The proposed robust model produced accurate point estimates for sensitivity (Se) and specificity (Sp) across various simulation parameters.
- It yielded narrower confidence intervals, indicating more precise estimates compared to standard models.
- The robust model produced similar estimates to standard models when no outlying studies were present.
Conclusions:
- The developed robust bivariate random-effects model offers reliable statistical inference for diagnostic test accuracy meta-analyses.
- It effectively handles outlying and influential studies, ensuring accurate and precise estimates of test performance.
- This approach enhances the validity of meta-analyses in medical research.
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